Top Machine Learning Jobs in Krakow

Assess and manage cybersecurity risks across AI systems, models, data pipelines, and supporting environments. Conduct AI security assessments, architecture reviews, threat modeling, and risk assessments; define lifecycle controls, standards, and guardrails; and support governance, monitoring, incident response, and remediation. The consultant will advise financial services clients on secure AI adoption while collaborating with security, data, engineering, architecture, risk, and business stakeholders. The role requires a one-year relocation to Qatar.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead software engineering efforts for Samsara’s External Platform EMEA, building scalable distributed systems, APIs, reporting, administration, integrations, and business-critical infrastructure. Lead technical discussions, influence architecture and roadmap, deliver full-stack features, maintain production systems, and support customer-focused iteration. Mentor engineers, hire and develop high-performing teams, and contribute to machine learning-powered products and long-running projects.

Own the roadmap and outcomes for data products supporting credit, BNPL, fraud, underwriting, acquiring, and payments decisions. Translate business, risk, and ML needs into buildable specifications, align delivery with Data Engineering and MLOps, and lead AI-first product practices. Manage up to three Data Consultants, capacity planning, stakeholder alignment, adoption metrics, data SLAs, and self-serve analytics across multiple product lines.

Leads the data and AI function for a new fintech platform, owning strategy, organization, budget, data engineering, ML/MLOps, data products, feature stores, agentic systems, governance, and real-time credit and risk decisioning. Builds the function from scratch, establishes production AI standards, oversees platform reliability and observability, partners on architecture and risk, and reports to executive and board-level stakeholders.

Own the vision, roadmap, and backlog for SEO and product content platforms within the Master Data domain. Drive scalable backend capabilities for metadata, content, faceted navigation, media management, structured data, crawling, indexing, and AI-supported discoverability. Collaborate with SEO, Content, Shop Management, Product, Data, and Engineering stakeholders, lead agile delivery, automate workflows, and improve discoverability, data quality, platform adoption, and operational efficiency.

Design, implement, optimize, and maintain complex large-scale software systems for airline network planning. Collaborate with globally distributed teams, solve architectural and algorithmic challenges, improve development processes and tooling, incorporate AI into workflows, mentor engineers, and drive software quality, stability, and operational excellence.

Design and implement data-driven applications and end-to-end data systems for clients. Responsibilities span cloud engineering, infrastructure, data engineering, ETL, machine learning, and data science. The role involves evaluating and presenting results, applying software engineering best practices, collaborating with data scientists and stakeholders, and delivering solutions through agile, iterative development.

Own the end-to-end strategy, roadmap, and outcomes for a network intelligence and security product serving ISP customers. Partner with engineering, data science, security, and compliance teams on architecture, ML evaluation, privacy, and production delivery. Conduct customer discovery, analyze market and threat trends, define requirements and metrics, build business cases, and represent the product to technical customers, executives, sales, marketing, and public audiences.

Lead Salesforce product initiatives supporting global sales teams. Translate seller and sales operations needs into requirements, roadmaps, business processes, and Salesforce solutions. Gather cross-regional feedback, oversee implementation and rollout, improve adoption, support reporting, and coordinate training and communications. Partner with sales, IT, architects, and technical teams while evaluating AI, machine learning, and new Salesforce capabilities to reduce administrative burden and improve business impact.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Build and lead production LLM systems, including agentic workflows, content enrichment, retrieval, semantic search, evaluation, observability, and safety guardrails. Own architecture decisions across model selection, prompting, fine-tuning, latency, cost, and quality. Partner with Product and UX to ship AI features, mentor engineers, lead technical reviews, and establish reusable LLM development practices.

Builds data engineering solutions for in-vehicle data quality, validation-drive automation, data triaging, and insights supporting hands-off urban driving. Responsibilities include developing APIs, cleaning and organizing raw and processed data, implementing analytics and machine learning solutions, and collaborating with engineering, product, and domain teams. The role involves cloud-based ETL and big-data systems connected to embedded driver-assistance technologies.

Processes investor transaction documents, maintains capital activity reports, completes AML requirements, prepares money movement notifications and wire instructions, and serves as a daily client contact. The role supports investor servicing operations, meets service-level deadlines, develops knowledge of allocations and performance calculations, mentors junior staff under supervision, and contributes to projects and ad hoc tasks.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Build, deploy, monitor, and improve production machine learning systems for customer and revenue use cases. Develop scalable data and ML pipelines, work with structured and unstructured data, apply modern ML and LLM techniques, and ensure model reliability through testing and validation. Collaborate with data engineers, product managers, and business stakeholders to deliver end-to-end solutions that improve business outcomes.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

Analyzes large datasets to identify platform performance and reliability issues, develops forecasting and early-warning systems, and builds software tools that automate analytical workflows. The role applies statistical analysis, machine learning, networking knowledge, and coding to diagnose content delivery challenges. It partners with Engineering, Operations, and Product teams to guide technology adoption and investment decisions. This is explicitly not a QA, DevOps, or Operations role.

Lead and grow the Money & Risk engineering team responsible for fraud prevention, risk scoring, reconciliation, settlement, and chargebacks. Own technical direction for scalable, auditable, secure payment systems across 80+ countries. Drive architecture, engineering standards, AI-assisted development, hiring, coaching, and cross-functional delivery with Product, Data, Finance, Compliance, and Operations.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.